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State-space stochastic volatility models: A review of estimation algorithms

APPLIED STOCHASTIC MODELS AND DATA ANALYSIS 1996年第4期12卷 265-279页

作者： Capobianco, E Department of Statistical Sciences University of Padua Italy

Stochastic volatility models (SVMs) represent an important framework for the analysis of financial time series data, together with ARCH-type models;but unlike the latter, the former, at least from the statistical point of view, cannot rely on the possibility of obtaining exact inference, in particular with regard to maximum likelihood estimates for the parameters of interest. For SVMs, usually only approximate results can be obtained, unless particularly sophisticated estimation strategies like exact non-gaussian filtering methods or simulation techniques are employed. In this paper we review SVM and present a new characterization for them, called 'generalized bilinear stochastic volatility'.

关键词： stochastic volatility linear and nonlinear state space representation simulation techniques generalized bilinear stochastic volatility processes estimation algorithms

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A stochastic approach to linear estimation in H_∞

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AUTOMATICA 1998年第4期34卷 405-414页

作者： Ninness, B Univ Newcastle Dept Elect & Comp Engn Callaghan NSW 2308 Australia

This paper examines the problem of system identification from frequency response data. Recent approaches to this problem, known collectively as 'estimation in H-infinity', involve deterministic descriptions of noise corruptions to the data. In order to provide 'worst-case' convergence with respect to these deterministic noise descriptions, non-linear data algorithms are required. In contrast, this paper examines 'worst-case' estimation in H-infinity when the disturbances are subject to mild stochastic assumptions and linearity in the data algorithms is employed. Issues of convergence, error bounds, and model order selection are considered. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： estimation theory estimation algorithms system identification robust estimation error analysis

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Ship track-keeping: experiments with a physical tanker model

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CONTROL ENGINEERING PRACTICE 1998年第6期6卷 763-769页

作者： Morawski, L Pomirski, J Gdynia Maritime Acad Dept Ship Automat PL-81225 Gdynia Poland

Two autopilots for ship track-keeping along a given trajectory are presented, namely a linear quadratic output feedback controller and a robust cascade controller. In both of them, the rudder is used as the only control actuator. The paper presents the mathematical foundation of the problem, the filtering techniques applied in its solution, and selected results of the simulations carried out on a physical ship model on the Silm Lake. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.

关键词： ship control tracking applications estimation algorithms output feedback H-infinity control

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Highlights of statistical signal and array processing

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IEEE SIGNAL PROCESSING MAGAZINE 1998年第5期15卷 21-64页

作者： Chen, TH Hero, A Djuric, PM Messer, H Goldberg, J Thomson, DJ Amin, MG Krim, H Pesquet, JC Giannakis, G Swami, A Tugnait, JK Cardoso, JF Tong, L Krolik, J Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Michigan Ann Arbor MI 48109 USA Univ Virginia Charlottesville VA 22903 USA Villanova Univ Villanova PA 19085 USA Ecole Natl Super Telecommun Bretagne Paris France Auburn Univ Auburn AL 36849 USA SUNY Stony Brook Stony Brook NY 11794 USA N Carolina State Univ Raleigh NC 27695 USA Duke Univ Durham NC 27706 USA Portland State Univ Portland OR 97207 USA Tel Aviv Univ IL-69978 Tel Aviv Israel Univ Illinois Chicago IL USA Univ Colorado Boulder CO 80309 USA USA Res Lab Washington DC 20310 USA Brigham Young Univ Provo UT 84602 USA Rochester Inst Technol Rochester NY 14623 USA Cornell Univ Ithaca NY 14853 USA Chalmers Univ Technol S-41296 Gothenburg Sweden Univ Texas Austin TX 78712 USA Purdue Univ W Lafayette IN 47907 USA

The Statistical Signal and Array Processing Technical Committee(SSAP-TC) deals with signals that are random and processes an array ofsignals simultaneously. The field of SSAP represents both solid theoryand practical applications. Starting with research in spectrumestimation and statistical modeling, study in this field is always fullof elegant mathematical tools such as statistical analysis and matrixtheory. The area of statistical signal processing expands intoestimation and detection algorithms, time-frequency domain analysis,system identification, and channel modeling and equalization. The areaof array signal processing also extends into multichannel filtering,source localization and separation, and so on. This article representsan endeavor by the members of the SSAT-TC to review all the significantdevelopments in the field of SSAP. To provide readers with pointers forfurther study of the field, this article includes a very impressivebibliography-close to 500 references are cited. This is just one of theindications that the field of statistical signals has been an extremelyactive one in the signal processing community. The article alsointroduces the recent reorganization of three technical committees ofthe Signal Processing Society

关键词： array signal processing parameter estimation reviews signal detection spectral analysis statistical analysis time-frequency analysis channel modeling detection algorithms equalization estimation algorithms matrix theory multichannel filtering random sign signal acquisition Parameter estimation channel modeling Statistical analysis array signal processing Array processing Statistical methods Fields Time frequency analysis Multichannel filter signals Spectrum Analysis Detection algorithms matrix theory Equalization

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Adaptive estimation Using Multiple Models and Neural Networks

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IFAC Proceedings Volumes 1998年第29期31卷 55-58页

作者： Tyrone L. Vincent Cecilia Galarza Pramod P. Khargonekar Engineering Colorado School of Mines Golden CO 80401 EECS Department University of Michigan Ann Arbor MI 48109 Fax: (303)273-3602

A method is presented to combine multiple model estimation with a neural network to obtain more accurate estimates. The key idea is to use the data from the initial phase of the run for system identification, and then run a single estimator designed for the identified model for the remainder of the run. The use of multiple models and neural networks allows the on-line identification to take place extremely quickly. The method is validated on actual data from an important estimation problem in microelectronics manufacturing which is subject to model uncertainties: determining end-point to an etch step using reflectometry data.

关键词： Adaptive algorithms Neural Networks estimation algorithms

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Towards estimation in the Sense of the Least Sum of Absolute Errors

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IFAC Proceedings Volumes 1998年第20期31卷 605-610页

作者： Krzysztof B. Janiszowski Institute of Industrial Automatic Control Warsaw Technical University 02 - 525 Warsaw ul. Narbutta 87 Poland

This paper presents an attempt to introduce an estimation algorithm that can lead to estimates of coefficients of a parametric model, close to the values detennined by the Least Sum of Absolute errors of the model output. Examples of LSA-estimation application presented here show specific and interesting features. This estimation is quite insensitive to instantaneous, even very powerful and non-symmetric disturbances. A theorem for the derivation the LSA-estimation algorithm, as a special modification of the weighted Least Sum of Square estimation is presented. Application of the LSA-algorithm and a comparison with LSS-estimation results is presented on examples of static and dynamic models of linear plants.

关键词： Absolute error criterion estimation algorithms recursive scheme

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A Multiple Model Extension to the Bootstrap Filter for Manoeuvring Targets

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IFAC Proceedings Volumes 1998年第4期31卷 23-28页

作者： Shaun Mcginnity George Irwin Advanced Control Engineering Research Group Dept of Electrical and Electronic Engineering The Queen’s University of Belfast Belfast UK BT9 5AH

The bootstrap filter is an algorithm for implementing recursive Bayesian estimation in which the state probability density functions are approximated and evolved by updating and predicting a set of samples. An extension to this algorithm for application to the multiple model target tracking problem is presented as an alternative to the Interacting Multiple Model(IMM) algorithm. The Markovian transition probability matrix is used to approximate the model branching of the optimal solution to this problem. Simulation results from a standard manoeuvring target application suggest improved performance during non-manoeuvre periods and comparable performance during manoeuvres. Although these results are for a linear, Gaussian system the proposed method is more generally applicable.

关键词： manoeuvring target estimation algorithms

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State‐space stochastic volatility models: A review of estimation algorithms

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Applied Stochastic Models in Business and Industry 1996年第4期12卷

作者： Enrico Capobianco Department of Statistical Sciences University of Padua Italy

Stochastic volatility models (SVMs) represent an important framework for the analysis of financial time series data, together with ARCH‐type models; but unlike the latter, the former, at least from the statistical point of view, cannot rely on the possibility of obtaining exact inference, in particular with regard to maximum likelihood estimates for the parameters of interest. For SVMs, usually only approximate results can be obtained, unless particularly sophisticated estimation strategies like exact non‐gaussian filtering methods or simulation techniques are employed. In this paper we review SVM and present a new characterization for them, called ‘generalized bilinear stochastic volatility’. © 1996 John Wiley & Sons, Ltd.

关键词： stochastic volatility linear and nonlinear state space representation simulation techniques generalized bilinear stochastic volatility processes estimation algorithms

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estimation of a variable rate of depreciation: A dummy variable approach

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Structural Change and Economic Dynamics 1997年第3期8.0卷 319-325页

作者： Prucha, Ingmar Department of Economics University of Maryland Tydings Hall College Park MD 20742 United States

Many important economic problems require measures of both physical and R&D capital. Except for some recent studies, there have been relatively few contributions in the literature that provide econometric estimates for the depreciation rates of physical and R&D capital. One reason for the relative paucity of such econometric studies may be that if the depreciation rates are viewed as unknown parameters, or as a functions of unknown parameters, then the corresponding stocks of physical and R&D capital are also unobserved, resulting in a formidable estimation problem. In the past, econometric estimates of the depreciation rates of physical and R&D capital were typically obtained from self-programmed estimation algorithms. In this note, an approach is introduced that permits the econometric estimation of constant and variable rates of depreciation of physical and R&D capital using standard econometric packages. © 1997 Elsevier Science B.V.

关键词： Depreciation rates estimation algorithms Physical and R&D capital

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Adaptive Windowing Discrete-Time Velocity estimation Techniques: Application to HAPTIC Interfaces

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IFAC Proceedings Volumes 1997年第20期30卷 445-451页

作者： Vincent Hayward Farrokh Janabi-Sharifi Chung-Shin Jason Chen Department of Electrical Engineering and Center for Intelligent Machines McGill University 3480 University Street Montréal PQ Canada H3A 2A7 http://www.cim.mcgill.ca/~hayward/Home.html

A method is described to estimate velocity from discrete and quantized position samples via adaptive windowing. It addresses the shortcomings of previously known methods which necessitate tradeoffs between noise reduction, control delay, estimate accuracy, reliability, computational load, transient preservation, and which cause difficulties with tuning. The method is optimal in the sense that it minimizes the velocity error variance while maximizes the accuracy of the estimates. The design of the estimator requires the selection of only one parameter, namely a bound on the noise. Simulation and experimental results are presented.

关键词： Adaptive digital filters Discrete time Closed loop control Computer interfaces estimation algorithms PD controllers Nonlinear filters Velocity Measurements

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